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Long non-coding RNAs annotation and identification of potential therapeutic targets in Schistosoma mansoni

Grant number: 18/19591-2
Support type:Scholarships in Brazil - Master
Effective date (Start): March 01, 2019
Effective date (End): February 29, 2020
Field of knowledge:Biological Sciences - Parasitology
Principal Investigator:Sergio Verjovski Almeida
Grantee:Lucas Ferreira Maciel
Home Institution: Instituto Butantan. Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil

Abstract

Schistosomiasis is a tropical neglected disease, caused by worms from the genus Schistosoma, with estimates of more than 250 million infected people worldwide. Praziquantel is the only effective drug in schistosomiasis treatment, and therefore new therapeutic targets and drugs that could negatively affect its reproductive biology have been studied. However, research done until now has not explored the potential of long non-coding RNAs (lncRNAs). LcnRNAs in S. mansoni were recently identified by three studies, but because each work used different tools and definitions, part of the lncRNAs is redundant and they were annotated against an obsolete protein-coding transcriptome version. Besides, these works used whole parasites; on the other hand, it has already been shown that lncRNAs have tissue- and cell-specific expression. Thus, our objective is to identify a robust and complete lncRNAs set, that agrees with the present transcriptome, and to re-analyze RNA-seq datasets still non-annotated for the presence of lncRNAs in order to understand the role of lncRNAs in this organism's biology and to identify potential new therapeutic targets. For this, all lncRNAs annotated until now will be compared to the gene sequences of the new proteins to remove pre-mRNAs, and their propensity to be an lncRNA will be re-evaluated by means of the FEELnc tool that uses a random forest algorithm. New lncRNAs in RNA-Seq datasets of single stem cells and of gonads will be identified with a pipeline involving reads mapping and transcripts reconstruction, classification with FEELnc and filtering of potential mRNAs. Based on this new lncRNAs final set, we will perform a quantitative re-analysis of public RNA-Seq datasets to identify differentially expressed genes in different conditions, either physiological or involving drug treatments, with a focus on lncRNAs. (AU)